Abstract:

Traditional optimal trajectories for lunar soft landing can’t be calculated in real time if there’s error when spacecraft entering orbit or during the descending stage. A novel scheme was proposed to replan the trajectory in real time for the random forest model. The descending stage was modeled by three phases: deboost, descend and land. The modelcan predict the control by the state of spacecraft as the input, the lander descend slower and the error gets smaller. The simulation results show that with a 500m error in entering orbit the distances deceases to 50m after the deboost phase. This error is in the range of the training set of descend phases, so after that the position error is less than 10m and the velocity error is less than 0.01m/s, both in tolerance. Meanwhile this method is fast enough to satisfy the real time control.

Key words: lunar softlanding, real-time control, supervised learning, random forest, dynamic , modeling, trajectory replan